04/07/2024 Erik Kusch
11
Erik Kusch
Senior Engineer
Machine Readable Nature Research Group (MANA)
Department of Research and Collections
Natural History Museum
University of Oslo
04/07/2024 Erik Kusch
ECOLOGICAL NETWORKS IN
MACROECOLOGICAL
RESEARCH
Enhancing Projections of Biodiversity in the Anthropocene
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
2
MOTIVATION & BACKGROUND
Biological Interactions and Macroecology in the Anthropocene
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
3Biodiversity Is Connected and Under Threat
Illustration credit: http://www.davidebonadonna.it/
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??
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Extinctions ripple through the biosphere via
ecological interactions.
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Anthropocene & Ecosphere
Environmental conditions
Expressions of species interactions
Species extinctions
...
The Ecosphere is being reshuffled
Local
Regional
Continental
Global
Changes
Scales
Macro-scale threat to ecological communities = threat to humans
Humanity is dependant on stability of
ecosystems & ecological communities
Effects and threats
across geographic scales
Macroecological perspective relevant for study-needs of the Anthropocene.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
4Central Research Question
Under the Anthropocene, how can we use ecological interactions to understand
ecosystem stability and forecast ecosystem change at macroecological scales?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
5
An ecological interaction is the effect of one species on...
Ecological interactions are highly complex:
Identity (present/absent/potential)
Sign (+/-)
Magnitude
Directionality (directed vs. undirected)
Ecological interactions can be abstracted:
Networks/Graphs:
Nodes/Vertices = Species Identities
Links/Edges = Interactions
Network Matrices:
Columns/Rows = Species Indetities
Cell values = Interactions
Ecological Interactions –Complexity & Abstraction
... the probability of occurrence of another species.
... the fitness of another co-occurring species.
A
B
C
D
E
A
-1
3
B
1
C
-3
D
-2
E
C
A
B
E
D
A) B)
A
B
C
D
E
A
-1
B
C3
D
E1
-3
-2
A) B)
C
A
B
E
D
Node
Link
Node
Ecological interactions can be studied via
ecological networks.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
6Central Research Needs –Limiting Scope
Under the Anthropocene, how can we use ecological networks to understand
ecosystem stability and forecast ecosystem change at macroecological scales?
Under the Anthropocene, how can we use ecological interactions to understand
ecosystem stability and forecast ecosystem change at macroecological scales?
Under the Anthropocene, how can we use ecological networks to understand
ecosystem stability and forecast ecosystem change at macroecological scales?
Explore likely extinction cascade
scenarios
Evaluate current macroecological
research and data practices
Evaluate statistical methodology for
macroecological networks
Updating Macroecological Rersearch
Practices.
Chapter I
Using Ecological Networks as Forecast
Tools.
Chapter II
Inferring Biological Interactions from
Proxies.
Chapter III
Climate Change. Extinction Cascades. Macroecological Networks.
I. The Anthropocene II. Ecosystem Change III. Macroecologial Scales
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
7
Updating Macroecological Research Practices
Chapter I
Evaluating current macroecological research and data practices
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
8The Holy Trinity of Abiotic Data in the 21st Century
Environments
…
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
9
Accuracy:
Good fit due to data assimilation practices
Provisioning of uncertainty metrics
Resolution:
Space: 9km (ERA5-Land)
Time: hourly intervals
ECVs offered by climate reanalyses:
e.g.: ERA5(-Land): ~83 variables
Covering all ECVs indexing important components of
ecosystems such as:
Atmosphere
Soil properties
Reformation: Climate Reanalyses are the Solution
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
1010
RESOLVING ROADBLOCKS TO
USING CLIMATE REANALYSIS DATA
IN RWITH KrigR
Published January 6, 2022
10
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
11 KrigR –Novel Climate Data Stream
Data Retrieval
Spatial Resolution
ERA5(-Land) data retrieval is too complex and
unintuitive for many users.
9x9km resolution is too coarse for some
downstream applications.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
12 KrigR –Novel Climate Data Stream (Downloads)
Data Retrieval
Era5(-Land)
Data / Spatial
product
More intuitive download
specification
Spatial data limiting beyond
extents
Aggregation to desired
temporal resolutions
Intuitive download specification Spatial data limiting Desired temporal resolutions & aggregate metrics
download_ERA() can take shapefiles and
point-locations.
Using the arguments TResolution,
TStep , and FUN download_ERA()
can aggregate time-series to any desired
temporal resolution.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
13 KrigR –Novel Climate Data Stream (Downscaling)
Data Retrieval
Era5(-Land)
Data / Spatial
product
Downscaled
Product
Downscaling
Standard Error
&
&Covariates
(Target Resolution)
Covariates
(Training Resolution) &
Kriging Covariates
Spatial Resolution
Downscaled
Product
Downscaling
Standard Error
&
Kriging
&Covariates
(Target Resolution)
Covariates
(Training Resolution) &
Covariates
KrigR provides USGS GMTED 2010 digital
elevation model data as interpolation covariates.
krigR() enables parallel processing of multi-layer
rasters and allows for pausing and restarting kriging via
temporary files.
Kriging uncertainty can
help understand quality
and robustness of
interpolated data.
9x9km resolution is too coarse for some
downstream applications.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
14 KrigR –Novel Climate Data Stream
Downscaled
Product
Downscaling
Standard Error
&
Kriging
&Covariates
(Target Resolution)
Covariates
(Training Resolution) &
Covariates
Downscaled
Product
Downscaling
Standard Error
&
&Covariates
(Target Resolution)
Covariates
(Training Resolution) &
Kriging Covariates
Spatial Resolution Data Retrieval
Era5(-Land)
Data / Spatial
product
Third-
party
data
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
15 Using KrigR for Macroecological Studies
Data Stream vs. Static Product
Benefit from any improvements to source data
Encourages interdisciplinary engagement
Customisation of Data Products
Unparalleled potential for data customisation
Temporal aggregation & metrics
ECV provisioning
Align data products with research questions
Uncertainty Indicators
First workflow fully reporting data uncertainty
Propagation into downstream analyses?
Establishing bias corrected projections with KrigR:
Projection products:
downscaled future –downscaled historical = anomalies
Reanalysis products:
anomalies + downscaled reanalysis = projected reanalysis
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
1616
REAL-WORLD APPLICATIONS OF
THE KrigR-PIPIELINE
Published November 15, 2021
16
I
Does Kriging perform well at statistically downscaling
macroecologically relevant data?
II
Do KrigR products differ significantly from static legacy
data products?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
17 Kriging Accuracy
Testing Kriging Accuracy:
1. Krig upscaled ERA5-Land data to native resolution
2. Difference of upscaled & interpolated product ()
3. Total uncertainty of kriged product:
3. Where Kriging is not the most accurate method,
it is the only one that produces uncertainty estimates.
1. Kriging outperforms most other interpolation methods.
2. Kriging is highly accurate for a variety of ECVs.
I
Does Kriging perform well at statistically downscaling
macroecologically relevant data?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
18 KrigR & Legacy Products
4. KrigR-products do not align with most legacy products.
5. Particularly, in topographically heterogenous regions, KrigR seems most
reliable (i.e. accurate) and informative (through provision of uncertainty
metrics) to us.
II
Do KrigR products differ significantly from static legacy
data products?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
19 Implications for macroecology
19
Instead, macroecology ought to undergo a paradigm shift towards data streams making use of newly developed,
state-of-the-art climate reanalysis data and novel methdology such as KrigR.
The practice of using static data sets reporting only a small number of ECVs at subpar temporal resolutions is not suited for
macroecological research needs of the Anthropocene.
I am cuyrrently re-developing/-deploying KrigR to:
1. Remove deprecated dependencies
2. Increase efficiency of downloads and target additional ECMWF products
3. Supply additional functionality and ready-made data products
You can follow development progress and install the latest version directly on/from GitHub:
https://github.com/ErikKusch/KrigR.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
20
Using Ecological Networks as Forecast Tools
Chapter II
Exploring likely extinction cascade scenarios
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
21 Extinction Cascades & Proxies of Threat
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Global
IUCN Red List assessments
…
Proxies for Risk of Primary Extinction
Localised
Climate Change & Projections:
Safety Margins
...
Placement in Network:
Centrality
…
What about secondary extinction risk?
P1 P2 P3
A1 A2 A3
P4
Initial Network LegendPost-Extinction Network
P1P2P3
A1A2A3
P4
I
Is choice of primary extinction risk
proxy relevant for extinction cascade
outcomes?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
22
Link-Loss-Sensitivity
% of combined initial interaction
magnitude a species requires for
continued existence
Rewiring Probability
Thresholds
Probability threshold which a
potential interaction needs to
exceed for establishment
Secondary Extinction Risk: Network Resilience
II
Does considering network resilience
metrics enhance extinction cascade
analyses?
Link-loss sensitivity and rewiring probability thresholds establish
two-dimensional landscapes of network resilience.
Post-Extinction Network
P1P2P3
A1A2A3
P4
P1P2P3
A1A2A3
P4
Contemporary Rworkflows fail to
incorporate link-loss sensitivity or
rewiring thresholds.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
2323
Published June 2, 2023
23
NETWORK RESILIENCE MECHANISMS
IN EXTINCTION CASCADE ANALYSES IN
RWITH NetworkExtinction
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
24 Package Scope and Functionality
NetworkExtinction supports:
Degree Distribution
Extinction Cascade Visualisations
Extinction Cascade Simulations
Static order:
Ordered
Random
Dynamic order
Mostconnected
Leastconnected
Supported Network Types:
Mutualistic
Trophic
Fully integrates:
Link-loss sensitvity
Rewiring Probability Thresholds
Defined either:
Whole-network
Individual nodes
Full exploration of network resilience
landscapes now possible.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
2525
EXPLORING EMPIRICAL NETWORK
RESILIENCE LANDSCAPES AT
MACROECOLOGICAL EXTENTS
Registered as preprint and undergoing preparation for resubmission
25
I
Is choice of primary extinction risk proxy relevant for
extinction cascade outcomes?
II
Does considering network resilience metrics enhance
extinction cascade analyses?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
26 Study Design & Primary Extinction Risk Proxies
Network Data
Plant-Frugivory Network Collection (Fricke et al., 2022)
406 networks
CMIP6 Projections KrigR rgbif
GBIF
1982-1999 Climatologies
SSP245 & SSP 585 2081-2100 1982-1999 Presence Records
1724 species
Climate Preferences & Niches
Species
… … … … …
… … … … …
… … … … …
Climate Safety Margins IUCN Centrality
IUCN
ConR
rredlist
igraph
Protected Areas
Extinction Risk Proxies
Networks for Analysis
81 networks
Species Richness
Interaction Strength Variation
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
27 Primary Extinction Species Pools
Global extinction risk proxies (i.e. IUCN) identify very few species for
primary extinction pools
Localised extinction risk proxies (i.e., Climate Safety Margins & Centrality)
identify much larger species pools for primary extinction
Considerable overlap between Centrality and Climate Safety Margin
classifications
1. Primary extinction risk proxies at too coarse a scale may underestimate
biodiversity loss at the level of individual empirical ecological networks.
SSP245
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
Climate Safety Margins IUCN Centrality
04/07/2024 Erik Kusch
28 Potential Networks & Rewiring Probability
Functional Trait Data
Functional Trait Expressions (Fricke et al., 2022)
124,179 records, 4715 species
Functional Expressions for Target Species
60,047 records, 1724 species
Subset for 1724
target species
identities
Plant Plant-Trait 1 Plant−Trait ... Animal Animal−Trait 1 Animal-Trait … Link Presence Network ID
… … … … … … … …
… … … … … … … …
… … … … … … … …
Rewiring Potential Identification
randomForest
Probability Matrices of Interactions between Target Species
Network 1 Plant A Plant B Plant …
Animal A … … …
Animal B … … …
Animal … … … …
Network 2 Plant A Plant B Plant …
Animal A ………
Animal B ………
Animal … … … …
Network … Plant A Plant B Plant …
Animal A … … …
Animal B … … …
Animal … … … … NetworkExtinction
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
Climate Safety Margins
IUCN
Centrality
04/07/2024 Erik Kusch
29 Drivers of Threat to Global Ecological Networks
2. Relative species loss post-extinction cascade simulation is considerably higher for
localised primary extinction risk proxies.
I
Is choice of primary extinction risk proxy relevant for
extinction cascade outcomes?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
Climate Safety Margins
IUCN
Centrality
Climate Safety Margins
04/07/2024 Erik Kusch
30 Empirical Network Resilience Landscapes
Network resilience landscapes demonstrate variation of
future ecosystem scenarios
Contemporary practices fail to explore these
Contemporary analysis assumptions:
Secondary extinction when all links are lost
No rewiring
Rewiring Probability Threshold
Relative Loss of Species
Link-Loss Sensitivity
Contemporary Extinction
Cascade Analyses
3. Contemporary simulations of extinction cascades likely underpredict
future biodiversity loss.
II
Does considering network resilience metrics enhance
extinction cascade analyses?
SSP245
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
31 Implications for ecological network forecasting
31
To use ecological networks for forecasting of ecological community structures, research ought to explore realistic ranges in the network
resilience landscapes of link-loss sensitivity & rewiring probability thresholds.
Neglecting to account for link-loss sensitivity and rewiring probability thresholds leads to a
likely underprediction of future biodiversity loss thus overestimating ecosystem stability.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
I am cuyrrently investigating relative contributions of rewiring and link-loss to post-simulation network makeup as well as effect sizes of
simulated effects.
04/07/2024 Erik Kusch
32
Inferring Biological Interactions from Proxies
Chapter III
Evaluating statistical methodology for macroecological networks
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
33
Observing ecological interactions is labour-intensive
Intractable at macroecological scales
Multiple frameworks for association/interaction inference have
been proposed
Most analyse co-occurrence patterns
Co-Occurrence based methods for association/interaction
inference have been critiqued
Research Questions:
Inference of Ecological Networks
Statistical inference of ecological networks
II
Are inference methods and
their networks scalable?
I
Does choice of inference method affect
inferred network structure?
III
How accurate is network inference?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
34 Philosophical Differences in Network Inference
Biodiversity Input Data Type
Consideration of
Environmental Conditions
Co-Occurrence Abundance Performance
Explicit
Implicit
None
Additional considerations of network inference:
Cosideration of Environmental Conditions:
Environmental conditions have been demonstrated to affect expressions of
interactions in identity and magnitude
Spectrum of Co-occurrence–Performance:
Statistically useable information content changes drastically when considering
presence/absence, abundance, or performance
All ecological network inference is built on spatial or temporal biodiversity data.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
3535
CONTRASTING ECOLOGICAL
NETWORK INFERENCE ACROSS
SCALES AND APPROACHES
Registered as preprint and under preparation for resubmission
35
II
Are inference methods and
their networks scalable?
I
Does choice of inference method affect
inferred network structure?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
36 Study Design & Data Streams
# 810,919 at 160401 plots
# 70,797 at 640 plots
Yosemite Forest Dynamics Plot
Yosemite National Park Temperate Conifer
Forest Biome
Pre-fire event in 2013
Raw
Data
Subsetting in
Time and Space
# 34,444 at 640 plots
11 species
Plot-Scale
# 291 at 101 plots
13 species
Region-Scale
# 96,169 at 46,328 plots
15 species
Macro-Scale
BIEN
YFDP Traits
KrigR
V.PhyloMaker
Plot Temperature Soil
Moisture
Precipi-
tation
Evapo-
ration
… … … … …
… … … … …
Species SLA Leaf Carbon Leaf Nitrogen
… … … …
… … … …
Plot Species 1 Species 2 Species …
… … … …
… … … …
Phylogeny
Distributional Null
Expectation
Environmental
Conditions
Spatial Products
Functional Trait
Data
Species Plot Performance Abundance Presence/ Absence
… … … … …
… … … … …
Adding Data for Use in
Ecological Network Inference
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
37 Network Inference Methods
Consideration of
Environmental Conditions
Co-Occurrence Abundance Performance
Explicit
Implicit
None
COOCCUR
NETASSOC
HMSC HMSC
HMSC
Biodiversity Input Data Type
NDD-RIM
COOCCUR
NETASSOC
HMSC
NDD-RIM
Bimler et al. 2023, Methods in Ecology and Evolution
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
38 No Consensus of Inference Across Approaches
I
Does choice of inference method
affect
inferred network structure?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
39 No Consensus Across Scales
II
Are inference methods and
their networks scalable?
NETASSOC
HMSC
COOCCUR
NDD-RIM
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
40 What Drives Inference Dissimilarity?
Consideration of environmental conditions
a driving factor of inference outcome at
macro scale?
Performance information may stabilise
inference outcome.
But how do we know which inference approach
yields the most accurate results?
III
How accurate is network inference?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
4141
EVALUATING NETWORK INFERENCE
PERFORMANCE USING SYNTHETIC
SPATIAL PRODUCTS
Registered as preprint and under review
41
III
How accurate is network inference?IV What drives differences in
network inference accuracy?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
42 Study Concept & Simulation Set-Up
Need to know the “true” ecological network to evaluate network
inference accuracy
Random generation of ecological network
Spatial biodiversity data analysed by the assessed inference
approach ought to reflect:
Environmental conditions & species-specific niche preferences
Interactions between individuals of interacting species
Demographic simulation with variable death rate:
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
43 Variable Death Rate Components
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
44 Network Realisation
True Potential Network True Realised Network
Environmental Preference Similarity
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
45 Network Inference
III
How accurate is network inference?
True Realised Network
Occurrence (O) –50% Abundance (A) –50% Performance (P) –30%
[O]Clim –30% [A]Clim –50% [P]Clim –30%
HMSC-Inferred Networks
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
46 Inference Accuracy
1. Network inference accuracy varies.
III
How accurate is network inference?
2. Gains to network inference accuracy when
considering abundance data and environmental
conditions.
Non-Abiotic Parameterisation Abiotic Parameterisation
All Parameterisations
Missed Absent Rate
Missed Negative Rate
Missed Positive Rate
True Absent Rate
True Negative Rate
True Positive Rate
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
47 Inference Likelihood
IV
What drives differences in
network inference accuracy?
[O] [A]Clim
Positive Associations
Negative Associations
[A]
3. Inference (irrespective of whether it is correct or not) of a positive association strongly depends on its strength and the
differences in environmental preference of the association partners. Negative associations are rarely inferred.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
48 Inference Accuracy
IV
What drives differences in
network inference accuracy?
[O] [A]Clim
Positive Associations
Negative Associations
[A]
4. Correct inference of an association (irrespective of sign) strongly depends on its strength and the differences in
environmental preference of the association partners.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
49 Implications macroecological network inference
49
There exists untapped development potential and need regarding ecological network
inference which may be addressed using simulation-based validation approaches.
Ecological network inference ought to be used with care at macroecological scales. To do so, I recommend alinging method choice with
research questions and data availability at the scale of assessment.
Inference of networks using empirical data results in cross-scale
inconsistencies with regards to inferred networks and their topologies.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
50
IMPLICATIONS & THE BIGGER PICTURE
Paradigm Shifts in Macroecology & Ecological Network Research
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
51 Implications & Development Needs
Updating Macroecological Rersearch Practices.
Chapter I
Using Ecological Networks as Forecast Tools.
Chapter II
Inferring Biological Interactions from Proxies.
Chapter III
Macroecologically must shift from static data products to data streams like KrigR to
address the research questions of the Anthropocene.
I continue to develop of KrigR:
- Widened support of data sets
- Shiny App deployment for easier access
Adopting ecological networks as forecasting tools while accounting for network resilience
mechanisms will enhance biodiversity projections.
NetworkExtinction shortcomings:
- Purely reductionist (no invasion events)
- Rewiring is assumed to be realised fully and
instantly
Inferring Biological Interactions from Proxies.
Avenues forward:
- Network inference method development
- Open Science standards for reporting & integrating
ecological interactions
Ecological network inference approaches ought to be be validated using synthetic data
before application to empiric research questions.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
52 Everything is a network – Let’s understand them
better!
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
53
Supplementary Slides
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
54 Inference Comparison - Modularity
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
55 Inference Comparison - Centrality
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
56 Method Development - Interactions in Context
?
?
vs
.
Shaped by a multitude of factors:
Climate
Do species share locations?
Phylogeny
Do species share evolutionary history?
Functional Traits
How similar are species?
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
57 Method Development –Intrinsic Fitness
Intrinsic Fitness:
Bayesian Method
Distributional estimates
Infer unobserved/rare interactions
Directed Effects
…
Fitness of focal individual
…
Intrinsic fitness by species
…
Interaction of j on
i
…
Abundance of species j in vicinity to
observation
i
Trade-off of abundance & individual fitness?
NDD-RIM
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
58
Environment
"Species may simply co
-occur because they favor the same abiotic conditions"
Method Development - Environment
Accounting for environmental variation:
Latent variables
Environmental information into model
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
59 Method Development - Phylogenies
Time-
Constraints
Species interactions are shaped through evolutionary time.
… Maximum Covariance
… Rate of decline in covariance
… Phylogenetic Distance
… Returns1 if i = j, and 0 otherwise
… Covariance of same-species interactions
Shared evolutionary history:
Affects interaction patterns of all types
Can be used to infer likely interactions
Application in R:
V.PhyloMaker R package
Phylogenies inform about evolutionary
history.
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
60 Method Development –Species Traits
Accounting for functional trait expressions:
Species-
Traits
Functional trait expressions of species can modulate species interactions.
What for?
Account for limiting
A posteriori exclusion of unlikely/impossible interactions
How?
Types of traits:
Functional
Demographic
Subset of available traits
Biological theory
Data-driven
Calculate pair-wise similarity of species
Nearest-Neighbour Distances
Hypervolume-Overlap
(Blonder, 2018)
Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
04/07/2024 Erik Kusch
61
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Helmholtz Centre for Environmental Research GmbH (UFZ) –Department Seminar
